/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.zhaohg.spark.examples.mllib;

// $example on$

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.GradientBoostedTrees;
import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
import org.apache.spark.mllib.util.MLUtils;
import scala.Tuple2;

import java.util.HashMap;
import java.util.Map;
// $example off$

public class JavaGradientBoostingRegressionExample {
    public static void main(String[] args) {
        // $example on$
        SparkConf sparkConf = new SparkConf()
                .setAppName("JavaGradientBoostedTreesRegressionExample");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        // Load and parse the data file.
        String datapath = "data/mllib/sample_libsvm_data.txt";
        JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();
        // Split the data into training and demo3 sets (30% held out for testing)
        JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
        JavaRDD<LabeledPoint> trainingData = splits[0];
        JavaRDD<LabeledPoint> testData = splits[1];
        
        // Train a GradientBoostedTrees model.
        // The defaultParams for Regression use SquaredError by default.
        BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Regression");
        boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
        boostingStrategy.getTreeStrategy().setMaxDepth(5);
        // Empty categoricalFeaturesInfo indicates all features are continuous.
        Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
        boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);
        
        final GradientBoostedTreesModel model =
                GradientBoostedTrees.train(trainingData, boostingStrategy);
        
        // Evaluate model on demo3 instances and compute demo3 error
        JavaPairRDD<Double, Double> predictionAndLabel =
                testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
                    @Override
                    public Tuple2<Double, Double> call(LabeledPoint p) {
                        return new Tuple2<>(model.predict(p.features()), p.label());
                    }
                });
        Double testMSE =
                predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
                    @Override
                    public Double call(Tuple2<Double, Double> pl) {
                        Double diff = pl._1() - pl._2();
                        return diff * diff;
                    }
                }).reduce(new Function2<Double, Double, Double>() {
                    @Override
                    public Double call(Double a, Double b) {
                        return a + b;
                    }
                }) / data.count();
        System.out.println("Test Mean Squared Error: " + testMSE);
        System.out.println("Learned regression GBT model:\n" + model.toDebugString());
        
        // Save and load model
        model.save(jsc.sc(), "target/tmp/myGradientBoostingRegressionModel");
        GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(),
                "target/tmp/myGradientBoostingRegressionModel");
        // $example off$
        
        jsc.stop();
    }
}
